Dimensionality reduction, segmentation, and quantification of multidimensional images: application to fluorescence microscopy

Noel Bonnet; Beatrice Raby; Jean-Marie Zahm

doi:10.1117/12.379592

14 March 2000 Dimensionality reduction, segmentation, and quantification of multidimensional images: application to fluorescence microscopy

Noel Bonnet, Beatrice Raby, Jean-Marie Zahm

Author Affiliations +

Proceedings Volume 3920, Spectral Imaging: Instrumentation, Applications, and Analysis; (2000) https://doi.org/10.1117/12.379592
Event: BiOS 2000 The International Symposium on Biomedical Optics, 2000, San Jose, CA, United States

Abstract

Fluorescence microscopy is rapidly becoming a multi- dimensional technique. Many applications generate similar data analysis problems. Whatever the non-spatial dimension (time, energy), users have to make the choice between local analysis and global analysis. For local analysis, the evolution of pixels (or regions of interest) is modeled as a function of the external parameter. Results are displayed as parametric images. For global analysis, multivariate statistical analysis can be used to extract and interpret the significant information (in the presence of redundancy and noise) in the form of eigenimages and eigenfactors. Automatic classification methods start to play a role for the co-location problem, in which pixels are classified into regions corresponding to positive, null or negative correlation. With two or three images, the scatterplot (an estimation of the joint probability density function), can be built. Interactive and automatic correlation partitioning (ICP, ACP) can then be performed. The method we have developed (Parzen estimate of the probability density function followed by the watersheds mathematical morphology approach) does not make assumptions about the shape of clusters. With more than three images, dimensionality reduction must be applied, for visualization purposes and for simplifying classification. This can be done by linear or non-linear methods such as Multi-Dimensional Scaling, Auto-Associative Neural Networks or Self-Organizing Mapping.

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Noel Bonnet, Beatrice Raby, and Jean-Marie Zahm "Dimensionality reduction, segmentation, and quantification of multidimensional images: application to fluorescence microscopy", Proc. SPIE 3920, Spectral Imaging: Instrumentation, Applications, and Analysis, (14 March 2000); https://doi.org/10.1117/12.379592

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